Multilevel models for predicting personal victimisation in England and Wales Andromachi Tseloni

1. Multilevel models for predicting personal victimisation in England and WalesAndromachi Tseloni Analysis of crime data ESRC Research Methods Festival 2010 St. Catherine’s College, Oxford, 5-8 July 2010

2. Outline • Victimisation theory and levels of analysis • Data • Dependent variable and covariates • Statistical specification • Modelling strategy • Results • Conclusions

3. Lifestyle & Routine Activity (Hindelang et al. 1978; Cohen and Felson 1979; Felson 1998) Individual Characteristics including Lifestyle Level 1 unit of analysis: individual Social Disorganisation (Shaw and McKay 1942; Sampson and Wooldregde 1987; Sampson and Groves 1989) Area Characteristics Level 2 unit of analysis: quarter postcode sector Victimisation theory

4. Data • Crimes, individual & household characteristics taken from the 2000 British Crime Survey • Incidents occurred within a 15´ walk from home to respondents who have not moved house in the previous year. • 905 areas (sampling points=quarter postcode sectors) • 4-29 households per sampling point (mean=10, standard deviation=5.9) • 15,774 individuals in total • Area characteristicstaken from the1991 Census Small Area Statistics • 5% random error, standardised values

5. Personal crimes • Common assault • Wounding • Robbery • Theft from person • Other theft from person • Sexual offences excluded • Series incidents are truncated at 5 events.

6. Figure 1: Personal Crimes across Areas (mean=0.8, skewness=2.9, concentration=2.0)

7. Figure 2: Personal Crimes across Individuals (mean=0.05, skewness=11.9, concentration=1.6)

8. Covariates:Household Level (1) • Demographic (male, age, non-white ethnicity) • Social (marital status, living with children, education, social class) • Tenure and accommodation type • Household Income • Length of residence in the area • Routine activities (away from home, going to pubs, clubs and drinking alcohol) • Area type (inner city, urban)

9. Covariates:Area Level (2) • 9 Regions of England and Wales (with South East=basis) • Percent (%) households renting privately • % Single adult non-pensioner households • % Afro-Caribbean • % Indian-subcontinent • % Population 16-24 years • % Households in housing association accommodation • % Households moved in the area last year • Population density • Poverty [0.859 percent lone parent households+0.887 percent households without car-0.758 nonmanual-0.877 percent owner occupied households+ 0.720 mean number of persons per room+0.889 percent households renting from LA].

10. Statistical Model • Multilevel negative binomial regression with extra negative binomial variation Cameron and Trivedi 1986 J. of Appl. EconometricsGoldstein 1995 Multilevel Statistical ModelsSnijders and Bosker 1999 Multilevel AnalysisTseloni 2000 J. of Quant. Crim. • ln ij=nij=Xij+q=pq=0 uqjzqij+q=Q-1q=p+1 uqjzqj i=1,...,15,774, j=1,...,905 (1)[uqj]~N(0,u)ln ij= nij+ eij (2)where exp(e0ij) follows a gamma probability distribution • E(Yij)= ij= exp( nij) & var(Yij)= ij+ 2ij / (3)/2overdispersion due to unexplained heterogeneity between individuals & 2/precision parameter

11. Modelling strategy Software MLwiN 2.11 Gradually added covariates The ones with p-value of χ12 < 0.10 were retained Five models fitted: Baseline model with just a random intercept Fixed individual and household effects Fixed individual, household and routine activities or lifestyle effects Fixed individual, household, lifestyle and area effects Fixed individual, household, lifestyle and area effects with fixed (cross-cluster) interactions

12. Baseline model Interpretation • Pecrijdenotes number of personal crimes • ij =0.046=exp(-3.074) is the estimated mean of personal crimes • α=2.206 & νare the parameters of overdispersion • var (u0j)=0.187, is the between areas variance of personal crimes which is non- significant

13. Final model

14. Interpretation of results: constant • ij =0.016=exp(-2.332-0.0442x51+0.00018x512) is the estimated number of personal crimes that a 51 years old married white woman without children is expected to experience per year. This woman has household income less than £30,000, goes to pubs less than 3 times per week and to clubs less often than once a week. Finally, she lives in her owned detached house for over 2 years in a rural area of England and Wales with national average population density and poverty.

15. Interpretation of results: Figure 3: Predicted personal crimes and individual’s age

16. Positive Single Divorced, especially with children Windowed in high population density areas Having children Social renters Private renters In terraced houses In flats or maisonettes Movers (less than 2 years in the same area) Negative Asian or black Non – definable social class Living in inner city Interpretation of results: significant socio-demographic effects

17. Lifestyle Men who go to clubs at least once per week Parents of children who go to pubs at least 3 times per week Area Poverty Population density Interpretation of results: significant lifestyle and area positive effects

18. Interpretation of results: Figure 4: Predicted personal crimes and area poverty

19. Interpretation of results: Figure 5: Predicted personal crimes and area population density

20. Interpretation of results: Figure 6: Predicted personal crimes for widowed individuals and others across area population density

21. Conclusions • Personal criminal victimisation is predicted by individual and area characteristics. • While significant unexplained heterogeneity between individuals remains, area information fully accounts for the area clustering of personal victimisation. • The results partly confirm the assertions of lifestyle /routine activities theory. Being male, non-white, inner city resident or having an outgoing lifestyle in general are exceptions. • The results also confirm the social disorganisation theory with respect to economic deprivation and population density. But they also showed that ethnic heterogeneity, residential mobility and high proportions of young population do not predict personal crimes. • The two theories should integrate into a single one as the effects of some risk factors on personal victimisation are communicable and/or depend on context.